inference_notebooks/inference_notebook.ipynb

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    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import os\n",
    "import sys\n",
    "# for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "#     for filename in filenames:\n",
    "#         print(os.path.join(dirname, filename))\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "import re\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "from datasets import Dataset as DS\n",
    "from datasets import load_metric\n",
    "from torchmetrics.text import WordErrorRate, CharErrorRate\n",
    "\n",
    "\n",
    "import random\n",
    "\n",
    "import time\n",
    "\n",
    "from sklearn.model_selection import train_test_split, KFold\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "\n",
    "\n",
    "import gc\n",
    "\n",
    "import inspect\n",
    "    \n",
    "from transformers import (\n",
    "    AutoTokenizer, \n",
    "    AutoModelForSeq2SeqLM,\n",
    "    DataCollatorForSeq2Seq,\n",
    "    Seq2SeqTrainer, \n",
    "    Seq2SeqTrainingArguments,\n",
    "    pipeline\n",
    ")\n",
    "    \n",
    "print(\"All libraries have been installed successfully!\", end=\"\\r\")"
   ]
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   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
   ]
  },
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   "source": [
    "test_df = pd.read_csv(\"/kaggle/input/full-regipa-dataset/testset.csv\")"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "alpha_pat = \"[a-zA-z0-9]\"\n",
    "\n",
    "test_df[\"Contents\"] = test_df[\"Contents\"].str.replace(alpha_pat, \"\", regex=True)"
   ]
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   "source": [
    "MODEL_NAME = \"teamapocalypseml/regben2ipa-byt5small\"\n",
    "# MODEL_NAME = \"teamapocalypseml/regben2ipa-umt5base\"\n",
    "# MODEL_NAME = \"teamapocalypseml/regben2ipa-mt5-base\""
   ]
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   "source": [
    "pipe = pipeline(\"text2text-generation\", model=MODEL_NAME, device=device)"
   ]
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   "source": [
    "texts = test_df[\"Contents\"].tolist()\n",
    "dists = test_df[\"District\"].tolist()"
   ]
  },
  {
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   "source": [
    "reformed_texts = [f\"<{dists[i]}> {texts[i]}\" for i in range(len(texts))]"
   ]
  },
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   "source": [
    "if \"byt5\" in MODEL_NAME:\n",
    "    ipas = pipe(reformed_texts, max_length=2048, batch_size=128)\n",
    "else:\n",
    "    ipas = pipe(reformed_texts, max_length=512, batch_size=8)\n",
    "gen_txt = []"
   ]
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   "source": [
    "for ipa in ipas:\n",
    "    gen_txt.append(ipa[\"generated_text\"])\n",
    "    torch.cuda.empty_cache()\n",
    "\n",
    "ipas = gen_txt\n",
    "del gen_txt\n",
    "gc.collect()"
   ]
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   "source": [
    "torch.cuda.empty_cache()"
   ]
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   "source": [
    "test_df[\"string\"] = ipas\n",
    "test_df = test_df.sort_index()"
   ]
  },
  {
   "cell_type": "code",
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   "id": "a130ec19",
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   "outputs": [],
   "source": [
    "preds = test_df[\"string\"].tolist()\n",
    "gts = test_df[\"IPA\"].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f88cd839",
   "metadata": {},
   "outputs": [],
   "source": [
    "cer = CharErrorRate()\n",
    "wer = WordErrorRate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d272580",
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   "source": [
    "wer_res = wer(preds, gts).item()\n",
    "cer_res = cer(preds, gts).item()"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "print(f\"\"\"\n",
    "    Word error rate: {wer_res},\n",
    "    Char error rate: {cer_res},\n",
    "\"\"\")"
   ]
  }
 ],
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